Qiang Li

Qiang Li

Assistant Professor,

Xidian University

Qiang Li is an assistant professor at Xidian University. He received his PhD degree from Sichuan University in 2023. He has published more than 30 papers on information displays. He serves as a Professional Committee of the Chinese Society for Optical Engineering and a member of the Three-Dimensional Imaging and Display Professional Committee of the China Society of Image and Graphics. His research focuses on the theories and technologies related to light field displays, AR displays, and intelligent image processing. He has led ten national and provincial research projects, such as the National Natural Science Foundation of China, the Natural Science Basic Research Program of Shaanxi, and the Xiaomi - 2025 Research Special Project. Dr. Li has served as a Guest Editor for the Photonics and as a reviewer for over ten international journals under publishers including Elsevier, Optica, IEEE, and Wiley. He has received more than sixty honors and awards.

Title

Intelligent three-dimensional processing and display of light field

Abstract：

The deep integration of artificial intelligence and optical imaging is driving 3D vision technologies toward unprecedented levels of accuracy, efficiency, and visual fidelity. In this context, this talk presents three recent advances from our group in intelligent light field processing and display. First, we propose LFSamba, a lightweight Mamba-based network for 3D salient object detection in light fields. By introducing a saliency-guided sequential scanning mechanism, LFSamba leverages Mamba’s linear-complexity global modeling capability to achieve over 60% higher detection accuracy while reducing model parameters by 50% compared to CNN- and Transformer-based methods. Second, to address the demand for high frame rates in light field displays, we design Triple I-3D Net, an interpolation network that synthesizes high-quality intermediate elemental image arrays through adaptive receptive fields and a unified motion-synthesis optimization strategy, significantly enhancing dynamic 3D display performance. Third, to mitigate voxel diffusion caused by rotational misalignments of the lens array, we develop PVGAN, a generative adversarial network based on homologous pixel–voxel distribution learning. PVGAN achieves, for the first time, quantitative modeling and end-to-end correction of coupled multi-error mechanisms. Notably, it enables batch-wise 3D reconstruction without iterative preprocessing, reaching a three to four times improvement in spatial resolution over the uncorrected system and substantially enhancing display quality in terms of sharpness. These works provide key technical support for applications such as light field medical visualization and immersive 3D display.